The O'Reilly Radar Podcast: Tim O'Reilly and Astro Teller talk about technology and society, and the importance of moonshots.

In this week’s Radar Podcast episode, Tim O’Reilly sits down with Google X’s Astro Teller. Their wide-ranging conversation covers moonshots, the relationship between technology and society, the learning process for hardware, and more. What follows are some snippets of their conversation to whet your appetite — you can listen to the entire interview in the SoundCloud player below, or download the podcast through Stitcher, TuneIn, or iTunes.

Technology doesn’t create net losses for the economy

Tim O’Reilly: The policy makers, I think, need to stop talking about creating jobs and start talking about the work we need to do in the world, because if you do that work, you do create jobs. I was struck by this when I went to Mount Vernon, George Washington’s home. He was really into scientific agriculture, as was Thomas Jefferson. He had this vision that America could feed the world. There was that economic vision: there is something that needs doing. One of the things I love about Google X is it’s driven by solving problems, and those problems actually often do create new opportunities for work.

Astro Teller: I completely agree with you about the problems. In addition, when you look at the history of technology — its introduction, and what happened in society afterword — technology has functioned in every case in the past as a lever for the human mind or for the human body. Things like the introduction of spreadsheets destroyed the business, the profession of bookkeeping — but because we trained people, we as society trained people, they became accountants, they became analysts. As many jobs as were lost were created, and more work, more productivity was created in the process. The bulldozer took away, in a very analogous way, a lot of jobs from people who were digging with shovels, but because we trained them to do things like build the bulldozers, drive the bulldozers, maintain the bulldozers, it wasn’t a net loss for the economy.

I believe that the failure mode we are currently in, to the extent that there’s a failure mode, is not the introduction of new technologies but the failure of our society to train the young people of the world so that they will be prepared to use these more and more sophisticated levers.

Moonshots, Decacorns, Leadership, and Deep Learning

How to Make Moonshots (Astro Teller) — Expecting a person to be a reliable backup for the [self-driving car] system was a fallacy. Once people trust the system, they trust it. Our success was itself a failure. We came quickly to the conclusion that we needed to make it clear to ourselves that the human was not a reliable backup — the car had to always be able to handle the situation. And the best way to make that clear was to design a car with no steering wheel — a car that could drive itself all of the time, from point A to point B, at the push of a button.

Billion-Dollar Math (Bloomberg) — There’s a new buzzword, “decacorn,” for those over $10 billion, which includes Airbnb, Dropbox, Pinterest, Snapchat, and Uber. It’s a made-up word based on a creature that doesn’t exist. “If you wake up in a room full of unicorns, you are dreaming,” Todd Dagres, a founding partner at Spark Capital, recently told Bloomberg News. Not just cute seeing our industry explained to the unwashed, but it’s the first time I’d seen decacorn. (The weather’s just dandy in my cave, thanks for asking).

What Impactful Engineering Leadership Looks Like — aside from the ugliness of “impactful,” notable for good advice. “When engineering management is done right, you’re focusing on three big things,” she says. “You’re directly supporting the people on your team; you’re managing execution and coordination across teams; and you’re stepping back to observe and evolve the broader organization and its processes as it grows.”

Interview with Google X Life Science’s Head (Medium) — I will have been here two years this March. In nineteen months we have been able to hire more than a hundred scientists to work on this. We’ve been able to build customized labs and get the equipment to make nanoparticles and decorate them and functionalize them. We’ve been able to strike up collaborations with MIT and Stanford and Duke. We’ve been able to initiate protocols and partnerships with companies like Novartis. We’ve been able to initiate trials like the baseline trial. This would be a good decade somewhere else. The power of focus and money.

Schooloscope Open Data Post-Mortem — The case of Schooloscope and the wider question of public access to school data challenges the belief that sunlight is the best disinfectant, that government transparency would always lead to better government, better results. It challenges the sentiments that see data as value-neutral and its representation as devoid of politics. In fact, access to school data exposes a sharp contrast between the private interest of the family (best education for my child) and the public interest of the government (best education for all citizens).

M-Lab Observatory — explorable data on the data experience (RTT, upload speed, etc) across different ISPs in different geographies over time.

Review Ninja — a lightweight code review tool that works with GitHub, providing a more structured way to use pull requests for code review. ReviewNinja dispenses with elaborate voting systems, and supports hassle-free committing and merging for acceptable changes.